Color Scales in Plotly¶

Plotly is a powerful data visualization library that offers a wide range of features and customization options.

One of the most important aspects of creating visually appealing and effective plots is the selection of colors.

Plotly offers a variety of color palettes, also known as cmaps, that can be used to style and customize different types of plots. These cmaps include a range of color schemes that can be used for different purposes, such as highlighting trends, grouping data, and creating gradients.

In this way, the use of color can greatly enhance the clarity and interpretability of your visualizations. Understanding the available color palettes and how to use them effectively is an important part of creating effective data visualizations with Plotly.

In this context, Plotly Express provides a simple and intuitive way to select and apply different cmaps to your plots, making it easy to create stunning visualizations that communicate your message effectively.

Datasets¶

Plotly Express provides a variety of datasets that can be used to create example visualizations and explore the capabilities of the library. Two popular datasets that are often used for this purpose are iris and election.

iris()¶

The iris dataset is a classic example in data science and machine learning, and is widely used as a benchmark for classification and clustering algorithms. It contains measurements of the sepal length, sepal width, petal length, and petal width for 150 samples of iris flowers, belonging to three different species: Iris setosa, Iris versicolor, and Iris virginica. In Plotly Express, this dataset can be loaded using the px.data.iris() function, and can be used to create a wide range of visualizations, such as scatter plots, box plots, or histograms.

In [2]:
import pandas as pd
import plotly.express as xp
df = xp.data.iris()
df.head(50)
Out[2]:
sepal_length sepal_width petal_length petal_width species species_id
0 5.1 3.5 1.4 0.2 setosa 1
1 4.9 3.0 1.4 0.2 setosa 1
2 4.7 3.2 1.3 0.2 setosa 1
3 4.6 3.1 1.5 0.2 setosa 1
4 5.0 3.6 1.4 0.2 setosa 1
5 5.4 3.9 1.7 0.4 setosa 1
6 4.6 3.4 1.4 0.3 setosa 1
7 5.0 3.4 1.5 0.2 setosa 1
8 4.4 2.9 1.4 0.2 setosa 1
9 4.9 3.1 1.5 0.1 setosa 1
10 5.4 3.7 1.5 0.2 setosa 1
11 4.8 3.4 1.6 0.2 setosa 1
12 4.8 3.0 1.4 0.1 setosa 1
13 4.3 3.0 1.1 0.1 setosa 1
14 5.8 4.0 1.2 0.2 setosa 1
15 5.7 4.4 1.5 0.4 setosa 1
16 5.4 3.9 1.3 0.4 setosa 1
17 5.1 3.5 1.4 0.3 setosa 1
18 5.7 3.8 1.7 0.3 setosa 1
19 5.1 3.8 1.5 0.3 setosa 1
20 5.4 3.4 1.7 0.2 setosa 1
21 5.1 3.7 1.5 0.4 setosa 1
22 4.6 3.6 1.0 0.2 setosa 1
23 5.1 3.3 1.7 0.5 setosa 1
24 4.8 3.4 1.9 0.2 setosa 1
25 5.0 3.0 1.6 0.2 setosa 1
26 5.0 3.4 1.6 0.4 setosa 1
27 5.2 3.5 1.5 0.2 setosa 1
28 5.2 3.4 1.4 0.2 setosa 1
29 4.7 3.2 1.6 0.2 setosa 1
30 4.8 3.1 1.6 0.2 setosa 1
31 5.4 3.4 1.5 0.4 setosa 1
32 5.2 4.1 1.5 0.1 setosa 1
33 5.5 4.2 1.4 0.2 setosa 1
34 4.9 3.1 1.5 0.1 setosa 1
35 5.0 3.2 1.2 0.2 setosa 1
36 5.5 3.5 1.3 0.2 setosa 1
37 4.9 3.1 1.5 0.1 setosa 1
38 4.4 3.0 1.3 0.2 setosa 1
39 5.1 3.4 1.5 0.2 setosa 1
40 5.0 3.5 1.3 0.3 setosa 1
41 4.5 2.3 1.3 0.3 setosa 1
42 4.4 3.2 1.3 0.2 setosa 1
43 5.0 3.5 1.6 0.6 setosa 1
44 5.1 3.8 1.9 0.4 setosa 1
45 4.8 3.0 1.4 0.3 setosa 1
46 5.1 3.8 1.6 0.2 setosa 1
47 4.6 3.2 1.4 0.2 setosa 1
48 5.3 3.7 1.5 0.2 setosa 1
49 5.0 3.3 1.4 0.2 setosa 1

election()¶

The election dataset, on the other hand, is a more recent and politically-oriented dataset that contains information on the 2012 US presidential election results at the county level. It includes data on the percentage of votes cast for each candidate, as well as demographic information such as population density, income, and race. This dataset can be loaded using the px.data.election() function in Plotly Express, and can be used to create a range of visualizations, such as choropleth maps, pie charts, and scatter plots.

In [3]:
df2 = xp.data.election()
df2 = df2.groupby('district', as_index=False).head(1).sort_values('total'
                                                       , ascending=False)
df2
Out[3]:
district Coderre Bergeron Joly total winner result district_id
11 131-Saint-Édouard 1827 6408 2815 11050 Bergeron majority 131
12 132-Étienne-Desmarteau 2331 5748 2788 10867 Bergeron majority 132
13 133-Vieux-Rosemont 2670 4962 3234 10866 Bergeron plurality 133
29 193-Villeray 2201 5819 2782 10802 Bergeron majority 193
4 112-DeLorimier 1770 5933 3044 10747 Bergeron majority 112
7 121-La Pointe-aux-Prairies 5456 1760 3330 10546 Coderre majority 121
48 71-Tétreaultville 3694 2589 3454 9737 Coderre plurality 71
16 141-Côte-de-Liesse 4308 1320 3959 9587 Coderre plurality 141
42 51-Sault-Saint-Louis 4201 1642 3717 9560 Coderre plurality 51
8 122-Pointe-aux-Trembles 4734 1879 2852 9465 Coderre majority 122
20 161-Saint-HenriPetite-BourgognePointe-Saint-Ch... 2432 3368 3578 9378 Joly plurality 161
10 13-Ahuntsic 2979 3430 2873 9282 Bergeron plurality 13
14 134-Marie-Victorin 3673 3155 2431 9259 Coderre plurality 134
22 171-ChamplainL'Île-des-Soeurs 3347 2562 3291 9200 Coderre plurality 171
49 72-MaisonneuveLongue-Pointe 2746 3250 3139 9135 Bergeron plurality 72
52 81-Marie-Clarac 6591 1085 1435 9111 Coderre majority 81
3 111-Mile-End 1734 4782 2514 9030 Bergeron majority 111
2 11-Sault-au-Récollet 3348 2770 2532 8650 Coderre plurality 11
19 152-Saint-Léonard-Ouest 5387 1184 1908 8479 Coderre majority 152
17 142-Norman-McLaren 4104 1459 2822 8385 Coderre plurality 142
9 123-Rivière-des-Prairies 5737 958 1656 8351 Coderre majority 123
6 12-Saint-Sulpice 3252 2521 2543 8316 Coderre plurality 12
53 82-Ovide-Clermont 6229 780 1051 8060 Coderre majority 82
51 74-Louis-Riel 3509 2178 2338 8025 Coderre plurality 74
23 172-Desmarchais-Crawford 2476 2631 2849 7956 Joly plurality 172
50 73-Hochelaga 1546 3679 2675 7900 Bergeron plurality 73
43 52-Cecil-P.-Newman 3536 1330 2943 7809 Coderre plurality 52
37 34-Notre-Dame-de-Grâce 1773 2653 3262 7688 Joly plurality 34
28 192-François-Perrault 2878 2666 2039 7583 Coderre plurality 192
5 113-Jeanne-Mance 1455 3599 2316 7370 Bergeron plurality 113
0 101-Bois-de-Liesse 2481 1829 3024 7334 Joly plurality 101
15 14-Bordeaux-Cartierville 3612 1554 2081 7247 Coderre plurality 14
21 162-Saint-PaulÉmard 2566 2092 2438 7096 Coderre plurality 162
18 151-Saint-Léonard-Est 3931 882 1641 6454 Coderre majority 151
26 183-Sainte-Marie 1347 2827 2271 6445 Bergeron plurality 183
1 102-Cap-Saint-Jacques 2525 1163 2675 6363 Joly plurality 102
25 182-Saint-Jacques 1906 2169 2282 6357 Joly plurality 182
38 35-Loyola 2040 1437 2648 6125 Joly plurality 35
27 191-Saint-Michel 3668 984 1220 5872 Coderre majority 191
30 194-Parc-Extension 2420 1793 1402 5615 Coderre plurality 194
35 32-Côte-des-Neiges 1644 1950 1578 5172 Bergeron plurality 32
36 33-Snowdon 1548 1503 1636 4687 Joly plurality 33
33 23-Centre 2526 851 1286 4663 Coderre majority 23
41 43-Fort-Rolland 1325 1205 1908 4438 Joly plurality 43
34 31-Darlington 1873 1182 1232 4287 Coderre plurality 31
24 181-Peter-McGill 1451 754 1894 4099 Joly plurality 181
31 21-Ouest 2184 691 1076 3951 Coderre majority 21
32 22-Est 1589 708 1172 3469 Coderre plurality 22
39 41-du Canal 1165 832 1266 3263 Joly plurality 41
40 42-J.-Émery-Provost 1193 653 1157 3003 Coderre plurality 42
54 91-Claude-Ryan 996 643 423 2062 Coderre plurality 91
55 92-Joseph-Beaubien 540 833 592 1965 Bergeron plurality 92
45 62-Denis-Benjamin-Viger 595 226 1068 1889 Joly majority 62
44 61-Pierre-Foretier 631 258 998 1887 Joly majority 61
57 94-Jeanne-Sauvé 491 698 489 1678 Bergeron plurality 94
46 63-Jacques-Bizard 518 224 690 1432 Joly plurality 63
56 93-Robert-Bourassa 446 465 419 1330 Bergeron plurality 93
47 64-Sainte-Geneviève 332 131 326 789 Coderre plurality 64

Both of these datasets provide rich and interesting data that can be used to explore the capabilities of Plotly and create visually appealing and informative visualizations. Additionally, Plotly provides a number of other datasets that can be used for similar purposes, such as the gapminder dataset, which contains data on the development indicators of countries over time, and the tips dataset, which contains data on restaurant tips and bills. These datasets can be accessed using the px.data module in Plotly, making it easy to experiment with different types of data and create engaging visualizations.

Sequential colors in plotly¶

In Plotly, the colors.sequential module provides users with a variety of pre-defined color scales that are designed for use in sequential data visualizations. These color scales are specifically created to effectively represent data sets that have a natural order, where one value is larger or smaller than another.

The colors.sequential module contains a wide range of color scales that can be used in different contexts. For example, the Blues scale includes a range of blue hues that gradually increase in intensity, making it ideal for visualizing data that increases over time or across a geographic area.

In [6]:
sequential.show()

colors.sequential info¶

This is a comprehensive dataset that includes all available color scales for the colors.sequential module in Plotly. This dataset contains color scales that are optimized for use in sequential data visualizations, making it ideal for creating informative and aesthetically pleasing plots.

The dataset includes:

  • scale: name for the sequential scale color.
  • size: ranging from 2 to 15, allowing users to choose the optimal number of colors for their specific data visualization needs.
  • codes: a list of RGB or hexadecimal color codes, making it easy for users to incorporate the color scales into their Plotly visualizations.
In [9]:
sequential_df
Out[9]:
scale size codes
47 Turbo 15 [#30123b, #4145ab, #4675ed, #39a2fc, #1bcfd4, ...
31 Plotly3 13 [#0508b8, #1910d8, #3c19f0, #6b1cfb, #981cfd, ...
65 turbid 12 [rgb(232, 245, 171), rgb(220, 219, 137), rgb(2...
58 haline 12 [rgb(41, 24, 107), rgb(42, 35, 160), rgb(15, 7...
53 algae 12 [rgb(214, 249, 207), rgb(186, 228, 174), rgb(1...
54 amp 12 [rgb(241, 236, 236), rgb(230, 209, 203), rgb(2...
55 deep 12 [rgb(253, 253, 204), rgb(206, 236, 179), rgb(1...
56 dense 12 [rgb(230, 240, 240), rgb(191, 221, 229), rgb(1...
57 gray 12 [rgb(0, 0, 0), rgb(16, 16, 16), rgb(38, 38, 38...
59 ice 12 [rgb(3, 5, 18), rgb(25, 25, 51), rgb(44, 42, 8...
60 matter 12 [rgb(253, 237, 176), rgb(250, 205, 145), rgb(2...
61 solar 12 [rgb(51, 19, 23), rgb(79, 28, 33), rgb(108, 36...
62 speed 12 [rgb(254, 252, 205), rgb(239, 225, 156), rgb(2...
63 tempo 12 [rgb(254, 245, 244), rgb(222, 224, 210), rgb(1...
64 thermal 12 [rgb(3, 35, 51), rgb(13, 48, 100), rgb(53, 50,...
39 RdBu 11 [rgb(103,0,31), rgb(178,24,43), rgb(214,96,77)...
48 Viridis 10 [#440154, #482878, #3e4989, #31688e, #26828e, ...
23 Magma 10 [#000004, #180f3d, #440f76, #721f81, #9e2f7f, ...
20 Inferno 10 [#000004, #1b0c41, #4a0c6b, #781c6d, #a52c60, ...
30 Plasma 10 [#0d0887, #46039f, #7201a8, #9c179e, #bd3786, ...
12 Cividis 10 [#00224e, #123570, #3b496c, #575d6d, #707173, ...
49 YlGn 9 [rgb(255,255,229), rgb(247,252,185), rgb(217,2...
50 YlGnBu 9 [rgb(255,255,217), rgb(237,248,177), rgb(199,2...
42 Reds 9 [rgb(255,245,240), rgb(254,224,210), rgb(252,1...
51 YlOrBr 9 [rgb(255,255,229), rgb(255,247,188), rgb(254,2...
40 RdPu 9 [rgb(255,247,243), rgb(253,224,221), rgb(252,1...
38 Rainbow 9 [rgb(150,0,90), rgb(0,0,200), rgb(0,25,255), r...
36 Purples 9 [rgb(252,251,253), rgb(239,237,245), rgb(218,2...
34 PuRd 9 [rgb(247,244,249), rgb(231,225,239), rgb(212,1...
32 PuBu 9 [rgb(255,247,251), rgb(236,231,242), rgb(208,2...
52 YlOrRd 9 [rgb(255,255,204), rgb(255,237,160), rgb(254,2...
33 PuBuGn 9 [rgb(255,247,251), rgb(236,226,240), rgb(208,2...
18 Greys 9 [rgb(255,255,255), rgb(240,240,240), rgb(217,2...
4 Blues 9 [rgb(247,251,255), rgb(222,235,247), rgb(198,2...
26 Oranges 9 [rgb(255,245,235), rgb(254,230,206), rgb(253,2...
25 OrRd 9 [rgb(255,247,236), rgb(254,232,200), rgb(253,2...
8 BuGn 9 [rgb(247,252,253), rgb(229,245,249), rgb(204,2...
9 BuPu 9 [rgb(247,252,253), rgb(224,236,244), rgb(191,2...
16 GnBu 9 [rgb(247,252,240), rgb(224,243,219), rgb(204,2...
17 Greens 9 [rgb(247,252,245), rgb(229,245,224), rgb(199,2...
1 Agsunset 7 [rgb(75, 41, 145), rgb(135, 44, 162), rgb(192,...
5 Blugrn 7 [rgb(196, 230, 195), rgb(150, 210, 164), rgb(1...
6 Bluyl 7 [rgb(247, 254, 174), rgb(183, 230, 165), rgb(1...
7 Brwnyl 7 [rgb(237, 229, 207), rgb(224, 194, 162), rgb(2...
10 Burg 7 [rgb(255, 198, 196), rgb(244, 163, 168), rgb(2...
11 Burgyl 7 [rgb(251, 230, 197), rgb(245, 186, 152), rgb(2...
13 Darkmint 7 [rgb(210, 251, 212), rgb(165, 219, 194), rgb(1...
15 Emrld 7 [rgb(211, 242, 163), rgb(151, 225, 150), rgb(1...
29 Pinkyl 7 [rgb(254, 246, 181), rgb(255, 221, 154), rgb(2...
41 Redor 7 [rgb(246, 210, 169), rgb(245, 183, 142), rgb(2...
22 Magenta 7 [rgb(243, 203, 211), rgb(234, 169, 189), rgb(2...
28 Peach 7 [rgb(253, 224, 197), rgb(250, 203, 166), rgb(2...
35 Purp 7 [rgb(243, 224, 247), rgb(228, 199, 241), rgb(2...
27 Oryel 7 [rgb(236, 218, 154), rgb(239, 196, 126), rgb(2...
37 Purpor 7 [rgb(249, 221, 218), rgb(242, 185, 196), rgb(2...
24 Mint 7 [rgb(228, 241, 225), rgb(180, 217, 204), rgb(1...
0 Aggrnyl 7 [rgb(36, 86, 104), rgb(15, 114, 121), rgb(13, ...
43 Sunset 7 [rgb(243, 231, 155), rgb(250, 196, 132), rgb(2...
44 Sunsetdark 7 [rgb(252, 222, 156), rgb(250, 164, 118), rgb(2...
45 Teal 7 [rgb(209, 238, 234), rgb(168, 219, 217), rgb(1...
46 Tealgrn 7 [rgb(176, 242, 188), rgb(137, 232, 172), rgb(1...
21 Jet 6 [rgb(0,0,131), rgb(0,60,170), rgb(5,255,255), ...
14 Electric 6 [rgb(0,0,0), rgb(30,0,100), rgb(120,0,100), rg...
2 Blackbody 5 [rgb(0,0,0), rgb(230,0,0), rgb(230,210,0), rgb...
19 Hot 4 [rgb(0,0,0), rgb(230,0,0), rgb(255,210,0), rgb...
3 Bluered 2 [rgb(0,0,255), rgb(255,0,0)] 
#normal scale
xp.colors.sequential.Viridis
#reverse scale _r
xp.colors.sequential.Viridis_r

Examples for color_continuous_scale¶

In [330]:
fig = xp.scatter(df,height=360, width=None,x='petal_length', y='sepal_length',
                color='petal_length', template= 'plotly_dark', 
                title= 'Viridis Scale', 
                color_continuous_scale= xp.colors.sequential.Viridis)

fig
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Examples for color_discrete_sequence¶

In [400]:
fig2 = xp.pie(df2.head(15),height=450, width=1000, hole=.40,
                names='district', values='total', color='district',
                 template= 'plotly_dark', title= 'Turbo : 15',
                 color_discrete_sequence= xp.colors.sequential.Turbo)
fig2
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Diverging colors in plotly¶

In Plotly, the colors.diverging module provides users with a range of pre-defined color scales that are optimized for use in diverging data visualizations. These color scales are specifically designed to effectively represent data sets where there is a clear midpoint or threshold, and where both positive and negative values are present.

The colors.diverging module includes a variety of color scales that can be used in different contexts. For example, the RdBu scale includes a range of red and blue hues that gradually increase in intensity in opposite directions, making it ideal for visualizing data with a clear midpoint or threshold. On the other hand, the Spectral scale includes a range of colors that gradually increase in intensity, but with more distinct hues, making it ideal for visualizing data with a more complex distribution.

In [469]:
xp.colors.diverging.swatches_continuous().update_layout(template='plotly_dark')

colors.diverging info¶

This is a comprehensive dataset that includes all available color scales for the colors.diverging module in Plotly. This dataset contains color scales that are optimized for use in sequential data visualizations, making it ideal for creating informative and aesthetically pleasing plots.

The dataset includes:

  • scale: name for the sequential scale color.
  • size: ranging from 5 to 12, allowing users to choose the optimal number of colors for their specific data visualization needs.
  • codes: a list of RGB color codes, making it easy for users to incorporate the color scales into their Plotly visualizations.
In [471]:
diverging_df
Out[471]:
scale size codes
21 oxy 12 [rgb(63, 5, 5), rgb(101, 6, 13), rgb(138, 17, ...
20 delta 12 [rgb(16, 31, 63), rgb(38, 62, 144), rgb(30, 11...
19 curl 12 [rgb(20, 29, 67), rgb(28, 72, 93), rgb(18, 115...
18 balance 12 [rgb(23, 28, 66), rgb(41, 58, 143), rgb(11, 10...
10 RdBu 11 [rgb(103,0,31), rgb(178,24,43), rgb(214,96,77)...
14 Spectral 11 [rgb(158,1,66), rgb(213,62,79), rgb(244,109,67...
13 RdYlGn 11 [rgb(165,0,38), rgb(215,48,39), rgb(244,109,67...
12 RdYlBu 11 [rgb(165,0,38), rgb(215,48,39), rgb(244,109,67...
1 BrBG 11 [rgb(84,48,5), rgb(140,81,10), rgb(191,129,45)...
11 RdGy 11 [rgb(103,0,31), rgb(178,24,43), rgb(214,96,77)...
9 PuOr 11 [rgb(127,59,8), rgb(179,88,6), rgb(224,130,20)...
7 Picnic 11 [rgb(0,0,255), rgb(51,153,255), rgb(102,204,25...
6 PiYG 11 [rgb(142,1,82), rgb(197,27,125), rgb(222,119,1...
5 PRGn 11 [rgb(64,0,75), rgb(118,42,131), rgb(153,112,17...
15 Tealrose 7 [rgb(0, 147, 146), rgb(114, 170, 161), rgb(177...
16 Temps 7 [rgb(0, 147, 146), rgb(57, 177, 133), rgb(156,...
17 Tropic 7 [rgb(0, 155, 158), rgb(66, 183, 185), rgb(167,...
4 Geyser 7 [rgb(0, 128, 128), rgb(112, 164, 148), rgb(180...
3 Fall 7 [rgb(61, 89, 65), rgb(119, 136, 104), rgb(181,...
2 Earth 7 [rgb(161, 105, 40), rgb(189, 146, 90), rgb(214...
0 Armyrose 7 [rgb(121, 130, 52), rgb(163, 173, 98), rgb(208...
8 Portland 5 [rgb(12,51,131), rgb(10,136,186), rgb(242,211,... 
#normal scale
xp.colors.diverging.delta
#reverse scale _r
xp.colors.diverging.delta_r
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%%html
<style>
p {
    text-align: justify;
}
tr:nth-child(odd) {
    background-color: darkgray; /* Color de fondo para filas impares */
}

tr:nth-child(even) {
    background-color: cadetblue; /* Color de fondo para filas pares */
}
th {
    color: gold !important;
}

.reveal {
    background: linear-gradient(45deg, black, cadetblue, white) !important;
}
.reveal p{
    color: white !important;
    text-align: justify !important;
    font-size: 33px;
    width: 85%;
}
.reveal ul, .reveal ol, .reveal li {
    color: white !important;
    font-size: 24px;
}
.reveal tr:nth-child(odd) {
    background-color: gray; /* Color de fondo para filas impares */
}

.reveal tr:nth-child(even) {
    background-color: cadetblue; /* Color de fondo para filas pares */
}
    .reveal .slides {
v   height: 95% !important;
    width: 90% !important;
}
.reveal .slides .fragment {
    opacity: 0;
    transition: opacity 1.5s ease-in-out;
}
</style>